Yes, there are apps available for macOS that can lock your laptop if your face is not detected after a set amount of time. Here are a few options:

1. Unlox: Unlox allows you to use your iPhone's Face ID or Apple Watch to automatically lock or unlock your Mac. You can configure it to lock your Mac if your face is not detected after a certain period, though it relies on the proximity of your iPhone or Apple Watch.

2. FaceLock for Mac: This app can lock your Mac if it doesn't detect your face after a set time. It uses your Mac's camera to recognize your face and automatically locks the screen when you step away.

3. Keylemon: Keylemon used to provide facial recognition-based locking for macOS. It allowed locking your Mac when your face was not detected, but it’s not clear if the app is still supported or updated for recent versions of macOS.

4. Using Script with Face Recognition: You can also set up a script using tools like AppleScript or Automator combined with facial recognition libraries (e.g., OpenCV) to create a custom solution that locks your Mac if your face is not detected within a specific time window.

If you're looking for something more robust and integrated with Face ID on macOS, Apple doesn't natively support this yet, but these apps provide solid alternatives. Would you like more details on how to set up any of these?

Creating a custom solution to lock your Mac based on face detection can be an interesting project. Here’s a high-level outline on how you can do it using Python and OpenCV, along with Automator or AppleScript to lock your Mac. Here's how you can get started:

Step 1: Install the Required Tools

You’ll need to install Python and the OpenCV library for facial recognition. If you don’t already have Python installed, you can install it via Homebrew or directly from Python's website.

1. Install Python (if needed):

   bash
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   brew install python
   

2. Install OpenCV:

   bash
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   pip install opencv-python
   

Step 2: Create a Face Detection Script

Here is a simple Python script using OpenCV to detect your face and take action if it’s not found for a certain period.

python
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import cv2
import time
import os

# Load the pre-trained Haar Cascade classifier for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Initialize variables
timeout = 60  # Set your timeout in seconds
face_not_detected_time = None

# Open a connection to your webcam
cap = cv2.VideoCapture(0)

while True:
    # Capture frame from webcam
    ret, frame = cap.read()
    
    # Convert to grayscale for face detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # Detect faces in the image
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    
    if len(faces) == 0:  # No faces detected
        if face_not_detected_time is None:
            face_not_detected_time = time.time()
        elif time.time() - face_not_detected_time >= timeout:
            # Lock the screen if face is not detected within the timeout
            os.system("pmset displaysleepnow")
            break
    else:  # Reset the timer if a face is detected
        face_not_detected_time = None
    
    # Exit the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the capture when everything is done
cap.release()
cv2.destroyAllWindows()

Step 3: Set Up Screen Lock

In the script, the line os.system("pmset displaysleepnow") locks the screen by putting the display to sleep, which effectively triggers the macOS lock screen. You can adjust this or use a more explicit method to lock the screen:

• Use AppleScript to lock the screen:

  bash
  Copy code
  os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")
  

This sends the "Ctrl+Cmd+Q" keystroke to lock the screen.

Step 4: Run the Script Automatically

To ensure this script runs automatically, you can add it as a background process. Here's one way to do it:

1. Create an Automator App:

   • Open Automator, select "Application", and add a "Run Shell Script" action.

   • In the "Run Shell Script" window, enter a command to run your Python script:

     bash
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     python /path/to/your/face_detect.py
     

   • Save the Automator app and set it to run at login.

2. Use launchd to start the script automatically: Create a plist file for launchd to run your script periodically or on startup.

   Example plist:

   xml
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   <?xml version="1.0" encoding="UTF-8"?>
   <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
   <plist version="1.0">
   <dict>
       <key>Label</key>
       <string>com.username.facedetect</string>
       <key>ProgramArguments</key>
       <array>
           <string>/usr/bin/python</string>
           <string>/path/to/your/face_detect.py</string>
       </array>
       <key>RunAtLoad</key>
       <true/>
       <key>KeepAlive</key>
       <true/>
   </dict>
   </plist>
   

   Save this file as ~/Library/LaunchAgents/com.username.facedetect.plist and load it with:

   bash
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   launchctl load ~/Library/LaunchAgents/com.username.facedetect.plist
   

This setup ensures your Mac will lock if your face isn’t detected within the specified timeout. Would you like help with more specific parts of this process, such as testing the face detection script?

To achieve the behavior where both facial recognition and your system password are required to log back in after a lock, you will need to combine the face detection mechanism with macOS's built-in password authentication for unlocking. Here's a conceptual approach you can take, though macOS doesn’t natively support custom biometric authentication for unlocking the system. So, we’ll implement a workaround:

Step 1: Implement Face Detection During Login Attempt

1. Modify the existing face detection script to work as a gatekeeper before you attempt to enter the system password. The idea is that the face detection needs to confirm your face before macOS prompts you for the system password.

2. Create a face authentication app that runs on startup and manages your login process. This app will check for your face before proceeding with a login.

Step 2: Locking the System and Requiring Both Face and Password

1. When the system locks (either because you manually lock it or because the face detection script locks it due to inactivity), you'll still need to enter your system password to unlock the system. macOS will handle the password part.

2. The face detection step should be triggered before macOS shows the login screen, ensuring that the login only proceeds if your face is detected. Here's how you can manage this:

Step 3: Modify the Python Script to Manage Face Detection Before Login

Extend the face detection script to allow the login process to continue only if your face is detected.

1. Face Detection Check Before Login: Modify the previous Python script to wait for the correct face detection before macOS shows the login screen.

   python
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   import cv2
   import os
   import time
   
   face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
   
   cap = cv2.VideoCapture(0)
   face_detected = False
   
   while not face_detected:
       ret, frame = cap.read()
       gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
       faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
       
       if len(faces) > 0:
           face_detected = True
           print("Face detected. Proceeding to login.")
           # Trigger system unlock after face detection
           os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")
           break
       else:
           print("No face detected. Waiting...")
       time.sleep(1)
   
   cap.release()
   

   This script runs in a loop and checks for your face. Once it detects your face, it triggers a lock-screen action (Ctrl+Cmd+Q) that sends you to the password screen. This gives you the two-step security: first, face detection, then your system password.

Step 4: Automate the Face and Password Login Workflow

To ensure that both face detection and password entry are required upon login, follow these steps:

1. Set up the face detection script to run at the login screen: You’ll have the script trigger whenever the system locks or is powered on. This can be set up via launchd to ensure that face detection happens before allowing the user to input their password.

   • Use the same approach with launchd as before to automatically start this script at login or screen lock.

2. Face Detection as Precondition for Unlocking: Since macOS always requires a password, the face detection script essentially acts as a precondition to unlocking. You won’t be able to interact with the lock screen until the face detection script passes and triggers the system’s lock-screen action.

Step 5: Test the Workflow

• Run the face detection script to ensure it correctly detects your face and proceeds to unlock the screen (prompting for the password).
• Make sure the script triggers when the system locks and allows for smooth transitioning between face detection and password entry.

This solution would ensure both facial recognition and your system password are required for access, though it’s a workaround to integrate facial detection with macOS’s default password security.

This looks like a well-thought-out plan for creating a face-detection-based locking system for your Mac laptop. Here are a few suggestions to make sure

To easily toggle the face-detection-based locking system on and off, along with adding a Bluetooth-based locking feature, you can follow these steps:

Step 1: Set Up a Hotkey for Toggling the Script

You can use a Python library like keyboard to set up a hotkey for toggling the face detection script. However, keyboard only works with administrative privileges on macOS, so it’s essential to run the script with elevated permissions. Here's how you can integrate it:

1. Install keyboard library:

   bash
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   pip install keyboard
   

2. Modify your script to include a toggle for running and pausing the face detection:

   python
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   import cv2
   import time
   import os
   import keyboard  # Hotkey management
   
   # Load the pre-trained Haar Cascade classifier for face detection
   face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
   
   # Initialize variables
   timeout = 60  # Set your timeout in seconds
   face_not_detected_time = None
   enabled = True  # Face detection enabled/disabled state
   
   # Open a connection to your webcam
   cap = cv2.VideoCapture(0)
   
   def toggle_face_detection():
       global enabled
       enabled = not enabled
       print("Face detection enabled" if enabled else "Face detection disabled")
   
   # Set a hotkey to toggle face detection (Ctrl + Shift + F)
   keyboard.add_hotkey('ctrl+shift+f', toggle_face_detection)
   
   while True:
       if enabled:
           # Capture frame from webcam
           ret, frame = cap.read()
           
           # Convert to grayscale for face detection
           gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
           
           # Detect faces in the image
           faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
           
           if len(faces) == 0:  # No faces detected
               if face_not_detected_time is None:
                   face_not_detected_time = time.time()
               elif time.time() - face_not_detected_time >= timeout:
                   # Lock the screen if face is not detected within the timeout
                   os.system("pmset displaysleepnow")
                   break
           else:  # Reset the timer if a face is detected
               face_not_detected_time = None
       
       # Exit the loop if 'q' is pressed
       if cv2.waitKey(1) & 0xFF == ord('q'):
           break
   
   # Release the capture when everything is done
   cap.release()
   cv2.destroyAllWindows()
   

This code allows you to toggle face detection on and off by pressing Ctrl + Shift + F without stopping the entire script.

Step 2: Bluetooth-Based Locking Feature

To lock your Mac when your phone disconnects, you can use the blueutil utility, which allows Bluetooth management from the terminal.

1. Install blueutil:

   bash
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   brew install blueutil
   

2. Create a script to monitor Bluetooth connection: This script continuously checks whether your phone is connected via Bluetooth and locks your screen if it disconnects.

   bash
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   #!/bin/bash
   
   # Replace 'XX-XX-XX-XX-XX-XX' with your phone's Bluetooth address
   DEVICE_ADDRESS="XX-XX-XX-XX-XX-XX"
   
   while true; do
       # Check if the device is connected
       blueutil --connected $DEVICE_ADDRESS | grep "0" > /dev/null
       if [ $? -eq 0 ]; then
           echo "Device disconnected. Locking screen..."
           osascript -e 'tell application "System Events" to keystroke "q" using {control down, command down}'
       else
           echo "Device connected."
       fi
       sleep 5  # Check every 5 seconds
   done
   

3. Find your phone’s Bluetooth address:

   Use blueutil to list all paired devices:

   bash
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   blueutil --paired
   

   Look for your phone’s address in the output.

4. Run both scripts together:

   You can run both the face detection script and the Bluetooth monitoring script in parallel, or combine them in a single Python script with subprocess calls.

Step 3: Automate Bluetooth Script

You can follow the same steps you used to create the launchd file for the face-detection script to automate the Bluetooth-based locking script as well. Create another .plist file for the Bluetooth script and load it using launchctl.

Would you like further assistance with integrating these two functionalities?

To modify your script to use Command + Shift + X as the hotkey, you can adjust the keyboard.add_hotkey() function to handle the Mac's command key. However, the Python keyboard library uses the term cmd for the Command key on Mac.

Here's how you can modify your script:

Step 1: Modify the Hotkey

Change the hotkey from Ctrl + Shift + F to Command + Shift + X using cmd+shift+x:

python
Copy code
keyboard.add_hotkey('cmd+shift+x', toggle_face_detection)

This will bind the toggle functionality to Command + Shift + X.

Step 2: Check if the Key Combination is Taken

To see if Command + Shift + X is already in use by another application, you can manually check your Mac's keyboard shortcuts by:

1. Go to System Preferences → Keyboard → Shortcuts.
2. Browse through the categories (such as "App Shortcuts", "Mission Control", and "Services") to see if Command + Shift + X is listed.

If you don’t see it being used by any existing shortcuts, you should be good to go. You can also try running the script to see if the key combo works as expected.

Updated Script Example

Here's the updated portion of your script with the new hotkey:

python
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import cv2
import time
import os
import keyboard  # Hotkey management

# Load the pre-trained Haar Cascade classifier for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Initialize variables
timeout = 60  # Set your timeout in seconds
face_not_detected_time = None
enabled = True  # Face detection enabled/disabled state

# Open a connection to your webcam
cap = cv2.VideoCapture(0)

def toggle_face_detection():
    global enabled
    enabled = not enabled
    print("Face detection enabled" if enabled else "Face detection disabled")

# Set a hotkey to toggle face detection (Command + Shift + X)
keyboard.add_hotkey('cmd+shift+x', toggle_face_detection)

while True:
    if enabled:
        # Capture frame from webcam
        ret, frame = cap.read()
        
        # Convert to grayscale for face detection
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # Detect faces in the image
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
        
        if len(faces) == 0:  # No faces detected
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                # Lock the screen if face is not detected within the timeout
                os.system("pmset displaysleepnow")
                break
        else:  # Reset the timer if a face is detected
            face_not_detected_time = None
    
    # Exit the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the capture when everything is done
cap.release()
cv2.destroyAllWindows()

You should now be able to toggle face detection on and off using Command + Shift + X. Would you like help with further testing or enhancements?

To make the script toggle face detection with Command + Shift + X and quit completely with Command + Shift + P, you'll need to set up two separate hotkeys for these actions. Here's how you can implement it:

Updated Script

1. Set Command + Shift + X to toggle face detection.
2. Set Command + Shift + P to quit the script entirely.

Here’s the updated version of your script:

python
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import cv2
import time
import os
import keyboard  # Hotkey management

# Load the pre-trained Haar Cascade classifier for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Initialize variables
timeout = 60  # Set your timeout in seconds
face_not_detected_time = None
enabled = True  # Face detection enabled/disabled state

# Open a connection to your webcam
cap = cv2.VideoCapture(0)

def toggle_face_detection():
    global enabled
    enabled = not enabled
    print("Face detection enabled" if enabled else "Face detection disabled")

def quit_script():
    print("Quitting the script...")
    cap.release()
    cv2.destroyAllWindows()
    os._exit(0)  # Exit the script

# Set a hotkey to toggle face detection (Command + Shift + X)
keyboard.add_hotkey('cmd+shift+x', toggle_face_detection)

# Set a hotkey to quit the script (Command + Shift + P)
keyboard.add_hotkey('cmd+shift+p', quit_script)

while True:
    if enabled:
        # Capture frame from webcam
        ret, frame = cap.read()
        
        # Convert to grayscale for face detection
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # Detect faces in the image
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
        
        if len(faces) == 0:  # No faces detected
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                # Lock the screen if face is not detected within the timeout
                os.system("pmset displaysleepnow")
                break
        else:  # Reset the timer if a face is detected
            face_not_detected_time = None
    
    # Add a small delay to avoid high CPU usage
    time.sleep(0.1)

Explanation:

• Command + Shift + X: Toggles the face detection feature on and off by setting the enabled flag.
• Command + Shift + P: Quits the script completely by releasing the webcam and closing the OpenCV window using cap.release() and cv2.destroyAllWindows(). It also calls os._exit(0) to exit the script.

Additional Notes:

1. Hotkeys: The keyboard.add_hotkey functions bind the Command + Shift + X and Command + Shift + P key combinations to toggle and quit actions, respectively.
2. Small CPU Load Delay: Adding time.sleep(0.1) to the loop reduces CPU usage.

This implementation should now allow you to toggle the face detection on/off with Command + Shift + X and quit the script with Command + Shift + P. Let me know if you need help testing or adjusting this!

Yes, the line:

python
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os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

is used to trigger the macOS shortcut for locking the screen (Control + Command + Q).

If you want the system to lock when your face is not detected (in addition to pmset displaysleepnow, which just sleeps the display), then you should replace or complement the pmset command with this osascript command in the face detection part of your script.

Updated Section of the Script

Replace this part of your script:

python
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# Lock the screen if face is not detected within the timeout
os.system("pmset displaysleepnow")

with:

python
Copy code
# Lock the screen if face is not detected within the timeout
os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

This will effectively trigger the macOS lock screen, asking for the system password (or your Apple Watch if you use it to unlock). The pmset command only puts the display to sleep, which may not invoke the lock screen immediately.

Final Example

python
Copy code
if len(faces) == 0:  # No faces detected
    if face_not_detected_time is None:
        face_not_detected_time = time.time()
    elif time.time() - face_not_detected_time >= timeout:
        # Lock the screen if face is not detected within the timeout
        os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")
        break

This ensures that if your face is not detected for the specified timeout period, the system will lock by simulating the Ctrl + Cmd + Q shortcut.

Should You Keep pmset displaysleepnow?

You can use both commands together if you want to ensure that the screen is both locked and put to sleep. However, it is typically enough to just lock the screen with osascript. If you want to combine them:

python
Copy code
os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")
os.system("pmset displaysleepnow")

This will first lock the screen and then put the display to sleep.

Let me know if this solution fits your needs!

To accomplish your goal of creating a Bluetooth-based locking tool that only starts when toggled with Command + Shift + B and does not automatically restart upon login, here’s how you can set it up:

Step 1: Script Installation

First, install blueutil using Homebrew, as you've already planned:

bash
Copy code
brew install blueutil

Step 2: Create the Bluetooth Lock Script

This script will check if your phone's Bluetooth connection is broken and lock the system. However, it will only start after the Command + Shift + B combination is pressed.

Create a file named bluetooth_lock.py with the following content:

python
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#!/usr/bin/env python3

import subprocess
import time
import os
import keyboard  # For hotkey management
import sys

# Bluetooth device MAC address
DEVICE_ADDRESS = sys.argv[1]

def check_bluetooth_connection():
    """Checks if the Bluetooth device is connected."""
    try:
        result = subprocess.run(
            ["blueutil", "--connected", DEVICE_ADDRESS],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE
        )
        return "0" in result.stdout.decode('utf-8')  # If the result is "0", the device is disconnected
    except Exception as e:
        print(f"Error checking Bluetooth connection: {e}")
        return False

def lock_screen():
    """Lock the macOS screen using AppleScript."""
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

def bluetooth_lock_loop():
    """Continuously checks the Bluetooth connection and locks the screen if the device is disconnected."""
    while True:
        if check_bluetooth_connection():
            print("Device disconnected. Locking screen...")
            lock_screen()
            break
        else:
            print("Device connected.")
        time.sleep(5)

# Set up hotkeys for starting/stopping the Bluetooth lock
bluetooth_lock_enabled = False

def toggle_bluetooth_lock():
    global bluetooth_lock_enabled
    bluetooth_lock_enabled = not bluetooth_lock_enabled
    print("Bluetooth lock enabled" if bluetooth_lock_enabled else "Bluetooth lock disabled")

    if bluetooth_lock_enabled:
        bluetooth_lock_loop()

# Set the hotkey to toggle Bluetooth lock (Command + Shift + B)
keyboard.add_hotkey('cmd+shift+b', toggle_bluetooth_lock)

# Run indefinitely to wait for hotkey input
print("Press Command + Shift + B to toggle the Bluetooth lock.")
keyboard.wait()  # This will keep the script running until terminated

Step 3: Manual Start Requirement and Hotkey

The above script uses Command + Shift + B to toggle the Bluetooth lock on and off. The tool will not automatically start upon login. Instead, the user must press Command + Shift + B to start the Bluetooth lock loop. The script uses the keyboard library to bind this hotkey.

Step 4: Create a launchd File for Manual Script Execution

Even though you don’t want the tool to run automatically upon login, you can still use launchd to easily launch the script manually when needed.

Create a plist file, for example, ~/Library/LaunchAgents/com.username.bluetoothlock.plist, with the following content:

xml
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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.username.bluetoothlock</string>
    <key>ProgramArguments</key>
    <array>
        <string>/usr/bin/python3</string>
        <string>/path/to/bluetooth_lock.py</string>
        <string>XX-XX-XX-XX-XX-XX</string> <!-- Replace this with your device's Bluetooth MAC address -->
    </array>
    <key>RunAtLoad</key>
    <false/>
    <key>KeepAlive</key>
    <false/>
</dict>
</plist>

This ensures that the script won’t automatically start upon login (RunAtLoad is set to false). You can load it manually with:

bash
Copy code
launchctl load ~/Library/LaunchAgents/com.username.bluetoothlock.plist

Step 5: Starting and Stopping the Bluetooth Lock

• To start the Bluetooth lock manually, load the plist and then press Command + Shift + B to enable the Bluetooth lock feature.
• To stop the Bluetooth lock manually, you can either press Command + Shift + B again to disable it, or use launchctl unload:

bash
Copy code
launchctl unload ~/Library/LaunchAgents/com.username.bluetoothlock.plist

Step 6: Make the Script Executable

Don’t forget to make your script executable by running:

bash
Copy code
chmod +x /path/to/bluetooth_lock.py

Testing

Test the script by running it manually or loading it through launchctl, and press Command + Shift + B to toggle the Bluetooth lock feature.

Let me know if you need further assistance with this setup!

Here’s how you can approach the implementation of the tools described in each section, including the environment setup, scripts, and hotkey toggles. I'll walk through each tool and the necessary steps to set them up:

mirrormirror

For times you wish to lock your MAC laptop if:

• Your face is not detected within a set period of time.
• Your Bluetooth device loses contact with your laptop.
• Your laptop moves more than a set distance in some period of time.

Face Off

Locks a MAC laptop if your face is not detected within a set period of time.

Verification Requirements:

• The script does not automatically run at boot or login but begins when the user presses Command + Shift + X. It can also be toggled off using the same hotkey. There is a small FO icon in the menu bar indicating the tool’s status.
• When the face is not detected, the tool provides a 15-second warning before locking the screen and closing applications. The user can only save work during the warning. The tool locks the screen after three failed detections and closes all apps, requiring re-authentication.
• The script doesn’t interfere with normal computer operations or cause performance issues.

Step 1: Create the Environment

bash
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conda create -n MIRRORMIRROR -c conda-forge opencv-python ipython pytesst keyboard blueutil

Step 2: Create the Face Detection Script

Name your script face_off.py:

python
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import cv2
import time
import os
import keyboard

# Face detection setup
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
timeout = 60
face_not_detected_time = None
enabled = True

# Toggle function
def toggle_face_detection():
    global enabled
    enabled = not enabled
    print("Face detection enabled" if enabled else "Face detection disabled")

keyboard.add_hotkey('cmd+shift+x', toggle_face_detection)

cap = cv2.VideoCapture(0)

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

while True:
    if enabled:
        ret, frame = cap.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
        
        if len(faces) == 0:
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                print("Face not detected, locking screen...")
                lock_screen()
                break
        else:
            face_not_detected_time = None

    time.sleep(0.1)

Step 3: Automate Hotkey and Menu Bar Icon

(You’ll need a macOS menu bar app library like pystray to implement the menu bar icon. Install via pip install pystray.)

bash
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conda install -n MIRRORMIRROR pystray

• Implement the pystray library to show a status icon in the menu bar. You can toggle the script's activity by clicking the icon or pressing the hotkey.

You Can Leave

Locks a MAC laptop if your Bluetooth device loses contact with your laptop.

Verification Requirements:

• The tool doesn’t start automatically but is toggled on with Command + Shift + B. There is a YCL menu bar icon.
• The tool warns the user 15 seconds before locking the screen, allowing the user to save their work. After disconnection, it closes all apps and locks the screen. It does not restart automatically after login.
• The user cannot turn off the tool during the warning period or after the Bluetooth connection is lost.

Step 1: Install blueutil

Install the utility required for Bluetooth management:

bash
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brew install blueutil

Step 2: Create the Bluetooth Lock Script

Name the script bluetooth_lock.py:

python
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import subprocess
import time
import os
import keyboard
import sys

DEVICE_ADDRESS = sys.argv[1]
bluetooth_lock_enabled = False

def check_bluetooth_connection():
    result = subprocess.run(["blueutil", "--connected", DEVICE_ADDRESS], stdout=subprocess.PIPE)
    return "0" in result.stdout.decode('utf-8')

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

def toggle_bluetooth_lock():
    global bluetooth_lock_enabled
    bluetooth_lock_enabled = not bluetooth_lock_enabled
    print("Bluetooth lock enabled" if bluetooth_lock_enabled else "Bluetooth lock disabled")

    while bluetooth_lock_enabled:
        if check_bluetooth_connection():
            print("Bluetooth disconnected, locking screen...")
            lock_screen()
            break
        time.sleep(5)

keyboard.add_hotkey('cmd+shift+b', toggle_bluetooth_lock)
keyboard.wait()

Step 3: Automate Hotkey and Menu Bar Icon

Implement the pystray library as described for the face detection tool to add the YCL icon to the menu bar.

But Don't Go Far

Locks a MAC laptop if it moves more than a set distance within a specified time period.

Verification Requirements:

• The tool is manually started with Command + Shift + G. There is a DGF icon in the menu bar.
• The tool prompts the user to specify a distance before locking and closing apps when the laptop moves beyond that range. It doesn’t restart after login.
• The user cannot turn off the tool after it detects movement beyond the set range.

Step 1: Geolocation Setup

You'll need to use a geolocation API (such as IP-based location services) to track the laptop's location and compare it with the previous location.

Step 2: Create the Movement Detection Script

Name the script distance_lock.py:

python
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import time
import os
import requests
import keyboard

current_location = None
distance_threshold = float(input("Enter the distance threshold (in miles): "))
move_lock_enabled = False

def get_current_location():
    response = requests.get("https://ipinfo.io/loc")  # Simple IP-based location service
    return tuple(map(float, response.text.strip().split(',')))

def calculate_distance(loc1, loc2):
    # Implement a simple formula to calculate distance between two lat/long points (e.g., Haversine)
    pass

def toggle_move_lock():
    global move_lock_enabled
    move_lock_enabled = not move_lock_enabled
    if move_lock_enabled:
        current_location = get_current_location()
        while move_lock_enabled:
            new_location = get_current_location()
            distance_moved = calculate_distance(current_location, new_location)
            if distance_moved > distance_threshold:
                print(f"Moved {distance_moved} miles. Locking screen...")
                lock_screen()
                break
            time.sleep(10)

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

keyboard.add_hotkey('cmd+shift+g', toggle_move_lock)
keyboard.wait()

Step 3: Automate Hotkey and Menu Bar Icon

Add the DGF icon using pystray, similar to the previous tools.

This outline gives you the steps to implement each tool, including creating the scripts, setting up hotkeys, and using menu bar icons for toggling.

To enable these three scripts to be toggled by their specified hotkeys (Command + Shift + X for Face Off, Command + Shift + B for You Can Leave, and Command + Shift + G for But Don't Go Far) and have corresponding menu bar icons for toggling on and off, you need to follow these steps:

1. Adding Hotkey Support with keyboard Module

Each script can be toggled on/off using the keyboard module to listen for hotkeys. This will allow users to toggle the scripts with their respective hotkeys.

2. Creating a Menu Bar Icon with pystray

The pystray library allows you to add a system tray (menu bar) icon that can be clicked to toggle the script's functionality. You can use this for all three tools. The menu bar item will serve as a visual indicator for the tool's status (enabled/disabled) and provide an alternative way to toggle the script.

General Steps to Enable Toggling for Each Tool:

1. Use keyboard for hotkey toggling: Bind the appropriate hotkey to start/stop each script.
2. Use pystray for menu bar icon: Create a menu bar icon that reflects the script's state and allows the user to toggle it on/off by clicking the icon.

Example Workflow for One Tool (Apply Similar Steps for All Three Tools)

Let’s take "Face Off" as an example. You can replicate the steps for the other two tools with their respective hotkeys and scripts.

Face Off Example:

1. Install Dependencies:

   Make sure you have pystray and keyboard installed in the MIRRORMIRROR environment:

   bash
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   conda install -n MIRRORMIRROR pystray
   pip install keyboard
   

2. Create the face_off.py Script:

Here is the complete script that toggles the face detection tool with Command + Shift + X and adds a menu bar icon to toggle it on/off:

python
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import cv2
import time
import os
import keyboard
import threading
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Face detection setup
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
timeout = 60
face_not_detected_time = None
enabled = False  # Start disabled by default

# Placeholder for the pystray icon, updated later
icon = None

# Function to start face detection
def start_face_detection():
    global face_not_detected_time
    cap = cv2.VideoCapture(0)
    while enabled:
        ret, frame = cap.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
        if len(faces) == 0:
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                print("Face not detected. Locking screen in 15 seconds.")
                time.sleep(15)  # Warning delay
                lock_screen()
                break
        else:
            face_not_detected_time = None
        time.sleep(0.1)  # Avoid high CPU usage
    cap.release()

def toggle_face_detection():
    global enabled
    enabled = not enabled
    if enabled:
        print("Face detection enabled.")
        threading.Thread(target=start_face_detection).start()
    else:
        print("Face detection disabled.")
    update_icon()

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Update the menu bar icon when the state changes
def update_icon():
    if enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "Face Off: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "Face Off: Disabled"

# Function to quit the app
def quit_app(icon, item):
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Face Detection', toggle_face_detection),
        item('Quit', quit_app)
    )
    icon = Icon("Face Off", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle face detection
keyboard.add_hotkey('cmd+shift+x', toggle_face_detection)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the script running indefinitely
keyboard.wait()

Explanation of the Script:

1. keyboard Hotkey (Command + Shift + X):

   • The script listens for the hotkey Command + Shift + X. When pressed, it toggles face detection on or off.

2. Menu Bar Icon (pystray):

   • When the tool is started, it adds a menu bar icon using pystray. The icon indicates whether the tool is enabled or disabled.
   • Clicking the icon brings up a menu where you can toggle the tool on/off or quit the app.

3. Image for the Menu Bar:

   • You'll need two images for the enabled/disabled states (enabled_icon.png and disabled_icon.png). These are the icons that will appear in the macOS menu bar.

4. Threading:

   • The face detection runs in a separate thread so that it doesn’t block the main thread handling the keyboard and menu bar actions.

Replicating the Process for the Other Tools:

1. For "You Can Leave" (Bluetooth):

   • Set the hotkey to Command + Shift + B instead of Command + Shift + X.
   • Implement a similar pystray icon for toggling the Bluetooth monitoring script on and off.

2. For "But Don't Go Far" (Distance):

   • Set the hotkey to Command + Shift + G.
   • Use pystray for the distance tracking tool with a similar toggling mechanism for starting/stopping the tool and handling the menu bar icon.

Automating the Icons and Hotkey Setup:

1. Creating the Icons:

   • You can create or download small PNG icons for "enabled" and "disabled" states. For example, you could use green for enabled and red for disabled.
   • Save them as enabled_icon.png and disabled_icon.png in the same directory as your scripts.

2. Running Each Script:

   • Run each script manually or use launchd to start the scripts on demand (not automatically) if needed. Ensure they don’t interfere with each other.

By following this method, you can toggle each script with the hotkeys or menu bar icons and visually indicate whether each tool is enabled or not. Let me know if you need further assistance with the specific implementation!

Here’s the full "You Can Leave" script that monitors your Bluetooth device connection and locks your Mac if the device disconnects. This script uses the hotkey Command + Shift + B to toggle the Bluetooth lock tool on and off and provides a menu bar icon for controlling it.

Full Script for "You Can Leave":

python
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import subprocess
import time
import os
import keyboard
import threading
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Bluetooth device address (replace with your device's MAC address)
DEVICE_ADDRESS = "XX-XX-XX-XX-XX-XX"
bluetooth_lock_enabled = False  # Start disabled by default
icon = None  # Placeholder for the menu bar icon

# Function to check if Bluetooth device is connected
def check_bluetooth_connection():
    try:
        result = subprocess.run(["blueutil", "--connected", DEVICE_ADDRESS], stdout=subprocess.PIPE)
        return "0" in result.stdout.decode('utf-8')  # If the result is "0", the device is disconnected
    except Exception as e:
        print(f"Error checking Bluetooth connection: {e}")
        return False

# Function to lock the screen
def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Function to start the Bluetooth monitoring loop
def bluetooth_lock_loop():
    global bluetooth_lock_enabled
    while bluetooth_lock_enabled:
        if check_bluetooth_connection():
            print("Bluetooth device disconnected, locking screen...")
            lock_screen()
            break
        else:
            print("Bluetooth device connected.")
        time.sleep(5)

# Function to toggle the Bluetooth lock tool
def toggle_bluetooth_lock():
    global bluetooth_lock_enabled
    bluetooth_lock_enabled = not bluetooth_lock_enabled
    print("Bluetooth lock enabled" if bluetooth_lock_enabled else "Bluetooth lock disabled")

    if bluetooth_lock_enabled:
        threading.Thread(target=bluetooth_lock_loop).start()
    update_icon()

# Update the menu bar icon when the tool state changes
def update_icon():
    if bluetooth_lock_enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "You Can Leave: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "You Can Leave: Disabled"

# Function to quit the app
def quit_app(icon, item):
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Bluetooth Lock', toggle_bluetooth_lock),
        item('Quit', quit_app)
    )
    icon = Icon("You Can Leave", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle the Bluetooth lock (Command + Shift + B)
keyboard.add_hotkey('cmd+shift+b', toggle_bluetooth_lock)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the script running indefinitely
keyboard.wait()

Explanation of the Script:

1. Bluetooth Check:

   • The check_bluetooth_connection() function uses blueutil to check if the specified Bluetooth device (your phone, for example) is connected. Replace "XX-XX-XX-XX-XX-XX" with your device's MAC address.
   • If the device is disconnected, it locks the screen using osascript to simulate Command + Control + Q, which locks the screen.

2. Hotkey Toggle:

   • The keyboard.add_hotkey() function binds Command + Shift + B to the toggle_bluetooth_lock() function. Pressing the hotkey starts/stops the Bluetooth monitoring loop.

3. Menu Bar Icon:

   • The pystray library is used to create a menu bar icon that shows whether the Bluetooth lock tool is enabled or disabled. You can click the icon to toggle the tool or quit the app.
   • You need two PNG images for the enabled and disabled states: enabled_icon.png and disabled_icon.png. These are the icons shown in the menu bar.

4. Threading:

   • The Bluetooth lock loop runs in a separate thread to avoid blocking the main script, which manages the hotkey and menu bar actions.

Requirements:

1. Install Required Libraries:

   • You need pystray and keyboard to enable the menu bar icon and hotkey functionality. Install them with:

   bash
   Copy code
   conda install -n MIRRORMIRROR pystray
   pip install keyboard
   

2. Create Icon Files:

   • Place two icon files, enabled_icon.png and disabled_icon.png, in the same directory as your script. You can use any small PNG icons that visually indicate the tool's enabled/disabled status.

3. Run the Script:

   • You can run the script manually or use launchd to run it on demand, but it won't start automatically unless you press Command + Shift + B to toggle it on.

How It Works:

1. The script does not automatically start when your Mac boots or when you log in. You can toggle it on/off by pressing Command + Shift + B.
2. When enabled, the tool continuously monitors the Bluetooth connection. If your device disconnects, it locks the screen.
3. The menu bar icon shows the current state (enabled/disabled) and can be clicked to toggle or quit the tool.
4. Quitting: Clicking "Quit" from the menu bar icon will stop the tool completely.

Customization:

• Timeout: You can adjust the time.sleep(5) interval in the bluetooth_lock_loop() function to change how frequently the script checks the Bluetooth connection.
• Icons: Feel free to customize the menu bar icons to suit your preferences.

This script ensures that the tool only runs when explicitly toggled on and won't restart automatically after a lock or reboot unless you activate it with Command + Shift + B.

Let me know if you need more help setting up or tweaking this!

Here is the full "But Don’t Go Far" script that locks your Mac if the laptop moves more than a set distance within a specific time period. The script uses the hotkey Command + Shift + G to toggle the distance monitoring tool on and off, with a menu bar icon to indicate the tool’s status.

Full Script for "But Don’t Go Far":

python
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import time
import os
import requests
import math
import keyboard
import threading
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Distance monitoring state
distance_monitor_enabled = False  # Start disabled by default
icon = None  # Placeholder for the menu bar icon

# Initial location and distance threshold
current_location = None
distance_threshold = 0.0  # Set when the tool is activated

# Function to get the current location using an IP geolocation API
def get_current_location():
    try:
        response = requests.get("https://ipinfo.io/loc")
        return tuple(map(float, response.text.strip().split(',')))  # Latitude, Longitude
    except Exception as e:
        print(f"Error getting location: {e}")
        return None

# Function to calculate distance between two lat/long points (Haversine formula)
def calculate_distance(loc1, loc2):
    R = 6371.0  # Earth radius in kilometers
    lat1, lon1 = math.radians(loc1[0]), math.radians(loc1[1])
    lat2, lon2 = math.radians(loc2[0]), math.radians(loc2[1])
    dlat = lat2 - lat1
    dlon = lon2 - lon1

    a = math.sin(dlat / 2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2)**2
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
    return R * c  # Distance in kilometers

# Function to lock the screen
def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Function to start monitoring the distance
def distance_monitor_loop():
    global current_location
    while distance_monitor_enabled:
        new_location = get_current_location()
        if new_location:
            distance_moved = calculate_distance(current_location, new_location)
            print(f"Distance moved: {distance_moved} km")
            if distance_moved > distance_threshold:
                print(f"Exceeded distance threshold of {distance_threshold} km. Locking screen...")
                time.sleep(15)  # Warning delay
                lock_screen()
                break
        else:
            print("Unable to get new location.")
        time.sleep(10)  # Check location every 10 seconds

# Function to toggle distance monitoring
def toggle_distance_monitor():
    global distance_monitor_enabled, current_location, distance_threshold
    distance_monitor_enabled = not distance_monitor_enabled
    if distance_monitor_enabled:
        print("Distance monitor enabled.")
        current_location = get_current_location()
        if current_location:
            distance_threshold = float(input("Enter the distance threshold (in kilometers): "))
            threading.Thread(target=distance_monitor_loop).start()
        else:
            print("Failed to get current location. Disabling monitor.")
            distance_monitor_enabled = False
    else:
        print("Distance monitor disabled.")
    update_icon()

# Update the menu bar icon when the tool state changes
def update_icon():
    if distance_monitor_enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "But Don't Go Far: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "But Don't Go Far: Disabled"

# Function to quit the app
def quit_app(icon, item):
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Distance Monitor', toggle_distance_monitor),
        item('Quit', quit_app)
    )
    icon = Icon("But Don't Go Far", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle the distance monitor (Command + Shift + G)
keyboard.add_hotkey('cmd+shift+g', toggle_distance_monitor)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the script running indefinitely
keyboard.wait()

Explanation of the Script:

1. Geolocation:

   • The script uses the https://ipinfo.io/loc API to get the current location of the laptop in latitude and longitude format. You could replace this with another location service if necessary.
   • The location is retrieved using IP-based geolocation, which is not as precise as GPS but works well for general location checks over a wide area.

2. Distance Calculation:

   • The calculate_distance() function uses the Haversine formula to calculate the distance between two sets of latitude and longitude coordinates, returning the distance in kilometers.
   • This is used to determine if the laptop has moved more than the set distance threshold.

3. Locking the Screen:

   • If the laptop moves beyond the set distance threshold, the script will lock the screen using osascript to simulate the Command + Control + Q key combination that locks the screen.

4. Hotkey Toggle:

   • The keyboard.add_hotkey() function binds Command + Shift + G to toggle the distance monitor on or off. When enabled, the script asks for the distance threshold (in kilometers) and starts monitoring.

5. Menu Bar Icon:

   • The pystray library is used to create a menu bar icon that reflects whether the tool is enabled or disabled. Clicking the icon allows you to toggle the tool or quit the app.
   • You need two icon images, enabled_icon.png and disabled_icon.png, to indicate the current status of the tool in the menu bar.

6. Threading:

   • The distance monitoring runs in a separate thread, so it doesn’t block the main thread that handles the hotkeys and menu bar actions.

Requirements:

1. Install Required Libraries:

   • You need pystray, requests, and keyboard to enable the menu bar icon, location services, and hotkey functionality. Install them with:

   bash
   Copy code
   conda install -n MIRRORMIRROR pystray requests
   pip install keyboard
   

2. Create Icon Files:

   • Place two icon files, enabled_icon.png and disabled_icon.png, in the same directory as your script. These icons represent the enabled and disabled states of the tool.

3. Run the Script:

   • You can run the script manually or use launchd to run it on demand. It will not start automatically; you need to press Command + Shift + G to activate the distance monitoring.

How It Works:

1. The script does not automatically start when your Mac boots or when you log in. You can toggle it on/off by pressing Command + Shift + G.
2. When enabled, the script asks for the distance threshold and starts monitoring the laptop’s location. If the laptop moves more than the set distance, it locks the screen after a 15-second warning.
3. The menu bar icon shows whether the tool is enabled or disabled. You can click the icon to toggle or quit the tool.
4. Quitting: Clicking "Quit" from the menu bar icon will stop the tool completely.

Customization:

• Location Source: You can replace the ipinfo.io API with another geolocation service if needed.
• Threshold Input: You could modify the script to provide a graphical input window instead of using input() to set the distance threshold.

This script ensures that the tool only runs when explicitly toggled on and won’t restart automatically after a lock or reboot unless you activate it with Command + Shift + G.

Let me know if you need any further assistance!

To ensure that all three tools start when the user logs into the UI but are toggled OFF by default, you can use launchd on macOS to automatically start the scripts. launchd can load your scripts at login but will allow the user to enable/disable them using the specified hotkeys (Command + Shift + X, Command + Shift + B, Command + Shift + G) or the menu bar icons.

Steps to Achieve This Setup

1. Create the Python Scripts:

   • Each tool should start in a disabled state by default, which is already handled by setting the enabled or bluetooth_lock_enabled or distance_monitor_enabled variables to False at the start of the scripts.

2. Create launchd Plist Files:

   • These will load each tool at login but leave the tool in a disabled state until toggled on by the user.

3. Install and Test the Tools:

   • Ensure the tools are installed and running under the MIRRORMIRROR environment.

Step 1: Create and Adjust the Scripts

Ensure your scripts for Face Off, You Can Leave, and But Don’t Go Far are set to start disabled by default:

• In the Face Off script:

  python
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  enabled = False  # Start disabled by default
  

• In the You Can Leave script:

  python
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  bluetooth_lock_enabled = False  # Start disabled by default
  

• In the But Don't Go Far script:

  python
  Copy code
  distance_monitor_enabled = False  # Start disabled by default
  

Step 2: Create launchd Plist Files for Each Tool

Create .plist files for each tool in ~/Library/LaunchAgents/. These .plist files will ensure that the scripts start when the user logs into the UI (but not during SSH login or other scenarios).

• For Face Off (Face Detection Tool):

  Create ~/Library/LaunchAgents/com.username.faceoff.plist:

  xml
  Copy code
  <?xml version="1.0" encoding="UTF-8"?>
  <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
  <plist version="1.0">
  <dict>
      <key>Label</key>
      <string>com.username.faceoff</string>
      <key>ProgramArguments</key>
      <array>
          <string>/path/to/conda/envs/MIRRORMIRROR/bin/python</string>
          <string>/path/to/face_off.py</string>
      </array>
      <key>RunAtLoad</key>
      <true/>
      <key>KeepAlive</key>
      <false/>
      <key>EnvironmentVariables</key>
      <dict>
          <key>PATH</key>
          <string>/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin</string>
      </dict>
  </dict>
  </plist>
  

  Replace /path/to/conda/envs/MIRRORMIRROR/bin/python with the actual path to Python in your conda environment, and /path/to/face_off.py with the path to your Face Off script.

• For You Can Leave (Bluetooth Monitoring Tool):

  Create ~/Library/LaunchAgents/com.username.youcanleave.plist:

  xml
  Copy code
  <?xml version="1.0" encoding="UTF-8"?>
  <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
  <plist version="1.0">
  <dict>
      <key>Label</key>
      <string>com.username.youcanleave</string>
      <key>ProgramArguments</key>
      <array>
          <string>/path/to/conda/envs/MIRRORMIRROR/bin/python</string>
          <string>/path/to/bluetooth_lock.py</string>
      </array>
      <key>RunAtLoad</key>
      <true/>
      <key>KeepAlive</key>
      <false/>
      <key>EnvironmentVariables</key>
      <dict>
          <key>PATH</key>
          <string>/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin</string>
      </dict>
  </dict>
  </plist>
  

• For But Don’t Go Far (Distance Monitoring Tool):

  Create ~/Library/LaunchAgents/com.username.butdontgofar.plist:

  xml
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  <?xml version="1.0" encoding="UTF-8"?>
  <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
  <plist version="1.0">
  <dict>
      <key>Label</key>
      <string>com.username.butdontgofar</string>
      <key>ProgramArguments</key>
      <array>
          <string>/path/to/conda/envs/MIRRORMIRROR/bin/python</string>
          <string>/path/to/distance_lock.py</string>
      </array>
      <key>RunAtLoad</key>
      <true/>
      <key>KeepAlive</key>
      <false/>
      <key>EnvironmentVariables</key>
      <dict>
          <key>PATH</key>
          <string>/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin</string>
      </dict>
  </dict>
  </plist>
  

Step 3: Load the launchd Plist Files

After creating the .plist files, load them into launchd to ensure they start at login. Run these commands in the terminal:

bash
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launchctl load ~/Library/LaunchAgents/com.username.faceoff.plist
launchctl load ~/Library/LaunchAgents/com.username.youcanleave.plist
launchctl load ~/Library/LaunchAgents/com.username.butdontgofar.plist

Step 4: Test the Scripts

Restart your computer or log out and back into the UI to confirm that the scripts are starting automatically (but in a disabled state). You can toggle each tool using the specified hotkeys:

• Face Off: Press Command + Shift + X to enable/disable.
• You Can Leave: Press Command + Shift + B to enable/disable.
• But Don’t Go Far: Press Command + Shift + G to enable/disable.

The corresponding menu bar icons should also allow you to control the tools.

This setup ensures that the scripts start automatically when you log into the UI, but they remain in a disabled state until manually toggled on via hotkeys or the menu bar icons. Let me know if you need any further assistance!

Yes, once you have loaded the launchd .plist files using the launchctl load command, the tools will automatically start every time your laptop boots, power cycles, or reboots.

What happens after loading:

• Autostart: When the laptop starts up or you log into the UI, macOS will automatically run the scripts specified in the .plist files.
• Toggled Off by Default: The scripts will start in the disabled state as defined in your Python scripts (enabled = False, etc.). They will only begin functioning when you manually toggle them on using the hotkeys or menu bar icons.
• No SSH Interference: These scripts

Your setup looks correct! Here's a quick review and confirmation of each section:

Face Off

• Description: The script locks the laptop if the face isn't detected within a set time.
• Hotkey: Command + Shift + X toggles the tool on/off.
• Menu Bar Icon: The FO icon shows the enabled/disabled state.
• Automation: The LaunchAgent will load the script at login but keep it in a disabled state by default.
• Plist: Your LaunchAgent XML file correctly points to the Python script and conda environment, ensuring the tool starts with login.

You Can Leave

• Description: Locks the laptop if the specified Bluetooth device disconnects.
• Hotkey: Command + Shift + B toggles the tool on/off.
• Menu Bar Icon: The YCL icon shows the enabled/disabled state.
• Automation: The script starts with the UI but remains disabled until the user manually toggles it on.
• Plist: Your XML file setup for launchd correctly runs the Bluetooth lock tool, starting disabled.

But Don't Go Far

• Description: Locks the laptop if it moves more than a set distance.
• Hotkey: Command + Shift + G toggles the tool on/off.
• Menu Bar Icon: The DGF icon shows the enabled/disabled state.
• Automation: The script starts disabled and can be toggled on when needed.
• Plist: The provided XML file will ensure the tool starts automatically when the user logs in, staying disabled until toggled.

General Review:

• Plist files: You correctly created .plist files for each script, ensuring they start at login but remain toggled off by default. The setup with launchctl is correct.
• Environment: The use of the MIRRORMIRROR conda environment is correctly referenced in each .plist file.
• Hotkeys & Icons: Each tool has its designated hotkey and corresponding menu bar icon for easy toggling, and these are correctly set up in your Python scripts.

Everything appears to be well-structured and should function as expected! Feel free to test the setup by rebooting and toggling the tools using the hotkeys or menu bar icons. Let me know if you run into any issues!

The current face_off.py script you provided uses basic face detection with OpenCV's Haar Cascade classifier, which detects any face but doesn’t distinguish between different faces (e.g., your face versus someone else's). To make the script recognize your face specifically, you need to implement face recognition in addition to face detection.

Here’s how you can modify your face_off.py script to only lock the screen if your face is not detected:

Step-by-Step Solution

1. Use Face Recognition Library: You can use a popular library called face_recognition, which is built on dlib and supports training and recognizing faces. It will allow you to recognize your specific face from the webcam feed.

2. Capture and Encode Your Face: First, you need to capture your face and create an encoding (a numerical representation of your face) that can be compared against future webcam frames.

3. Compare Faces: During each detection, the script will check if the detected face matches your saved face encoding.

Steps to Implement

Step 1: Install face_recognition Library

You need to install the face_recognition library along with its dependencies.

bash
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pip install face_recognition

Make sure it is installed in the MIRRORMIRROR environment.

Step 2: Capture and Save Your Face Encoding

Before modifying the script, you need to capture and save your face encoding. Create a new script called save_face_encoding.py to do this:

python
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import face_recognition
import cv2
import numpy as np
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

print("Look at the camera to capture your face...")
ret, frame = cap.read()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Detect face locations and encode your face
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

if face_encodings:
    # Save the face encoding to a file
    with open('your_face_encoding.pkl', 'wb') as f:
        pickle.dump(face_encodings[0], f)
    print("Face encoding saved successfully!")
else:
    print("No face detected, try again.")

cap.release()

Run this script to save your face encoding to a file (your_face_encoding.pkl). Ensure you're in good lighting conditions and looking directly at the camera.

Step 3: Modify face_off.py to Recognize Your Face

Here’s the modified version of face_off.py to detect your specific face using the saved face encoding:

python
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import cv2
import time
import os
import keyboard
import threading
import face_recognition
import pickle
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Load your face encoding
with open('your_face_encoding.pkl', 'rb') as f:
    my_face_encoding = pickle.load(f)

timeout = 60  # Timeout before locking
face_not_detected_time = None
enabled = False  # Start disabled by default

# Placeholder for the pystray icon, updated later
icon = None

# Function to start face recognition
def start_face_recognition():
    global face_not_detected_time
    cap = cv2.VideoCapture(0)
    while enabled:
        ret, frame = cap.read()
        if not ret:
            continue

        # Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
        rgb_frame = frame[:, :, ::-1]

        # Detect face locations and encode faces in the frame
        face_locations = face_recognition.face_locations(rgb_frame)
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

        face_recognized = False
        for face_encoding in face_encodings:
            # Compare the detected face with your saved encoding
            matches = face_recognition.compare_faces([my_face_encoding], face_encoding)
            if matches[0]:
                face_recognized = True
                break

        if face_recognized:
            face_not_detected_time = None
            print("Your face is detected.")
        else:
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                print("Your face is not detected. Locking screen in 15 seconds.")
                time.sleep(15)  # Warning delay
                lock_screen()
                break

        time.sleep(0.1)  # Slight delay to reduce CPU usage

    cap.release()

def toggle_face_recognition():
    global enabled
    enabled = not enabled
    if enabled:
        print("Face recognition enabled.")
        threading.Thread(target=start_face_recognition).start()
    else:
        print("Face recognition disabled.")
    update_icon()

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Update the menu bar icon when the state changes
def update_icon():
    if enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "Face Off: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "Face Off: Disabled"

# Function to quit the app
def quit_app(icon, item):
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Face Recognition', toggle_face_recognition),
        item('Quit', quit_app)
    )
    icon = Icon("Face Off", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle face recognition
keyboard.add_hotkey('cmd+shift+x', toggle_face_recognition)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the script running indefinitely
keyboard.wait()

Explanation of Changes:

1. Face Encoding:

   • The script uses the face encoding stored in your_face_encoding.pkl to identify your face.

2. Face Recognition:

   • It compares each detected face with your saved face encoding using face_recognition.compare_faces().
   • If your face is not detected within the timeout period (60 seconds), the script locks the screen.

3. Warnings:

   • If your face is not recognized, the script gives a 15-second warning before locking the screen.

Additional Enhancements:

• Multiple Faces: You can extend this by saving multiple face encodings to allow more than one person to be recognized.
• Accuracy: Adjust the face_recognition.compare_faces() tolerance to tweak the strictness of the match.

Final Thoughts:

This approach ensures that only your face (or a saved trusted face) will prevent the screen from locking, improving security over the basic face detection you initially had.

Would you like more guidance on any of the steps or features?

To adjust the strictness of face recognition (i.e., how closely a detected face needs to match your saved face encoding), you can modify the tolerance parameter in the face_recognition.compare_faces() function.

By default, the compare_faces() function uses a tolerance of 0.6, meaning that if the difference between the face encodings is less than or equal to 0.6, it will consider the faces a match. If you want to make it less strict, you can increase this value slightly. On the other hand, to make it more strict, you can decrease the tolerance.

How to Adjust the Strictness:

You can change the tolerance value when calling the compare_faces() function in your script. Here's how to do it:

python
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matches = face_recognition.compare_faces([my_face_encoding], face_encoding, tolerance=0.65)

In this example, I’ve increased the tolerance to 0.65, making the comparison less strict. You can adjust this value based on your needs.

Modified Code with Adjustable Strictness:

python
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import cv2
import time
import os
import keyboard
import threading
import face_recognition
import pickle
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Load your face encoding
with open('your_face_encoding.pkl', 'rb') as f:
    my_face_encoding = pickle.load(f)

timeout = 60  # Timeout before locking
face_not_detected_time = None
enabled = False  # Start disabled by default

# Tolerance setting for face recognition (higher = less strict, lower = more strict)
tolerance = 0.65  # Adjust this value to control strictness

# Placeholder for the pystray icon, updated later
icon = None

# Function to start face recognition
def start_face_recognition():
    global face_not_detected_time
    cap = cv2.VideoCapture(0)
    while enabled:
        ret, frame = cap.read()
        if not ret:
            continue

        # Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
        rgb_frame = frame[:, :, ::-1]

        # Detect face locations and encode faces in the frame
        face_locations = face_recognition.face_locations(rgb_frame)
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

        face_recognized = False
        for face_encoding in face_encodings:
            # Compare the detected face with your saved encoding with custom tolerance
            matches = face_recognition.compare_faces([my_face_encoding], face_encoding, tolerance=tolerance)
            if matches[0]:
                face_recognized = True
                break

        if face_recognized:
            face_not_detected_time = None
            print("Your face is detected.")
        else:
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                print("Your face is not detected. Locking screen in 15 seconds.")
                time.sleep(15)  # Warning delay
                lock_screen()
                break

        time.sleep(0.1)  # Slight delay to reduce CPU usage

    cap.release()

def toggle_face_recognition():
    global enabled
    enabled = not enabled
    if enabled:
        print("Face recognition enabled.")
        threading.Thread(target=start_face_recognition).start()
    else:
        print("Face recognition disabled.")
    update_icon()

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Update the menu bar icon when the state changes
def update_icon():
    if enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "Face Off: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "Face Off: Disabled"

# Function to quit the app
def quit_app(icon, item):
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Face Recognition', toggle_face_recognition),
        item('Quit', quit_app)
    )
    icon = Icon("Face Off", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle face recognition
keyboard.add_hotkey('cmd+shift+x', toggle_face_recognition)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the

To adjust the frequency of the face recognition check to run every 5 minutes, you can modify the script to include a delay between each face check. The current script performs face detection continuously in a loop without a significant delay.

Steps to make the face check run every 5 minutes:

1. Add a 5-minute delay between face recognition attempts by using the time.sleep() function inside the loop.

Here's your modified script that checks every 5 minutes (300 seconds):

python
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import cv2
import time
import os
import keyboard
import threading
import face_recognition
import pickle
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Load your face encoding
with open('your_face_encoding.pkl', 'rb') as f:
    my_face_encoding = pickle.load(f)

timeout = 60  # Timeout before locking
face_not_detected_time = None
enabled = False  # Start disabled by default

# Placeholder for the pystray icon, updated later
icon = None

# Function to start face recognition
def start_face_recognition():
    global face_not_detected_time
    cap = cv2.VideoCapture(0)
    while enabled:
        ret, frame = cap.read()
        if not ret:
            continue

        # Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
        rgb_frame = frame[:, :, ::-1]

        # Detect face locations and encode faces in the frame
        face_locations = face_recognition.face_locations(rgb_frame)
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

        face_recognized = False
        for face_encoding in face_encodings:
            # Compare the detected face with your saved encoding
            matches = face_recognition.compare_faces([my_face_encoding], face_encoding, tolerance=0.65)
            if matches[0]:
                face_recognized = True
                break

        if face_recognized:
            face_not_detected_time = None
            print("Your face is detected.")
        else:
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                print("Your face is not detected. Locking screen in 15 seconds.")
                time.sleep(15)  # Warning delay
                lock_screen()
                break

        cap.release()  # Release the camera after the check

        # Wait for 5 minutes before the next check (300 seconds)
        time.sleep(300)

def toggle_face_recognition():
    global enabled
    enabled = not enabled
    if enabled:
        print("Face recognition enabled.")
        threading.Thread(target=start_face_recognition).start()
    else:
        print("Face recognition disabled.")
    update_icon()

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Update the menu bar icon when the state changes
def update_icon():
    if enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "Face Off: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "Face Off: Disabled"

# Function to quit the app
def quit_app(icon, item):
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Face Recognition', toggle_face_recognition),
        item('Quit', quit_app)
    )
    icon = Icon("Face Off", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle face recognition
keyboard.add_hotkey('cmd+shift+x', toggle_face_recognition)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the script running indefinitely
keyboard.wait()

Key Changes:

1. 5-minute delay: The line time.sleep(300) causes the face recognition check to run every 5 minutes (300 seconds). After each face detection attempt (whether successful or not), the script waits for 5 minutes before trying again.
2. Camera release: The camera is released after each check using cap.release() to

The time.sleep(0.1) line was originally included to add a slight delay between each frame in the continuous face detection loop to reduce CPU usage. In the original implementation, the script was constantly reading frames from the webcam without any pauses, which could lead to excessive CPU usage and overheating, especially in a real-time video processing loop.

Why It Was There:

1. CPU Efficiency: The small 0.1-second delay ensures the script doesn’t consume too many system resources by constantly polling the camera and performing face recognition.
2. Smoother Processing: It gives the system a little break between each frame, which helps to make the face detection loop more efficient without significantly impacting responsiveness.

Why It’s No Longer Needed:

Since you want the face check to occur every 5 minutes, this continuous loop behavior is no longer necessary. In the updated script, you only check once every 5 minutes and release the camera immediately afterward. Therefore, the time.sleep(0.1) line is no longer required for this use case.

Updated Code Without time.sleep(0.1):

python
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import cv2
import time
import os
import keyboard
import threading
import face_recognition
import pickle
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Load your face encoding
with open('your_face_encoding.pkl', 'rb') as f:
    my_face_encoding = pickle.load(f)

timeout = 60  # Timeout before locking
face_not_detected_time = None
enabled = False  # Start disabled by default

# Placeholder for the pystray icon, updated later
icon = None

# Function to start face recognition
def start_face_recognition():
    global face_not_detected_time
    cap = cv2.VideoCapture(0)
    while enabled:
        ret, frame = cap.read()
        if not ret:
            continue

        # Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
        rgb_frame = frame[:, :, ::-1]

        # Detect face locations and encode faces in the frame
        face_locations = face_recognition.face_locations(rgb_frame)
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

        face_recognized = False
        for face_encoding in face_encodings:
            # Compare the detected face with your saved encoding
            matches = face_recognition.compare_faces([my_face_encoding], face_encoding, tolerance=0.65)
            if matches[0]:
                face_recognized = True
                break

        if face_recognized:
            face_not_detected_time = None
            print("Your face is detected.")
        else:
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                print("Your face is not detected. Locking screen in 15 seconds.")
                time.sleep(15)  # Warning delay
                lock_screen()
                break

        cap.release()  # Release the camera after the check

        # Wait for 5 minutes before the next check (300 seconds)
        time.sleep(300)

def toggle_face_recognition():
    global enabled
    enabled = not enabled
    if enabled:
        print("Face recognition enabled.")
        threading.Thread(target=start_face_recognition).start()
    else:
        print("Face recognition disabled.")
    update_icon()

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Update the menu bar icon when the state changes
def update_icon():
    if enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "Face Off: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "Face Off: Disabled"

# Function to quit the app
def quit_app(icon, item):
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Face Recognition', toggle_face_recognition),
        item('Quit', quit_app)
    )
    icon = Icon("Face Off", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle face recognition
keyboard.add_hotkey('cmd+shift+x', toggle_face_recognition)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the script running indefinitely
keyboard.wait()

In Summary:

• The time.sleep(0.1) was originally there to reduce CPU usage in a continuous detection loop.
• Since you're now running the check every 5 minutes (with time.sleep(300)), the small 0.1-second delay between frames is no longer necessary. Each check happens once, the camera is released, and the next check occurs after 5 minutes.

The line time.sleep(0.1) in your original script serves a specific purpose within the continuous loop where face recognition is being processed. It's designed to:

1. Reduce CPU Usage: Without a small delay in the loop, the script would constantly and immediately process frames from the webcam without any pause. This would use up more CPU resources than necessary because it's working as fast as possible in a tight loop.

2. Allow Other System Processes: By adding the small sleep, you give the system a moment to breathe between processing frames, reducing the overall load on your machine.

In the updated version where checks only occur every 5 minutes:

Since you're now checking every 5 minutes, the line time.sleep(0.1) is no longer necessary. Each cycle will only happen every 5 minutes with time.sleep(300) handling the timing between checks, and the CPU won't be constantly running intensive face detection in real time.

What to Do with time.sleep(0.1):

In this new implementation, you can remove the time.sleep(0.1) because:

• You're already pausing the entire face recognition loop for 5 minutes.
• There is no need for micro-pauses between continuous face detection since the face check only happens once every 5 minutes now.

Final Updated Script (with time.sleep(0.1) removed):

python
Copy code
import cv2
import time
import os
import keyboard
import threading
import face_recognition
import pickle
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Load your face encoding
with open('your_face_encoding.pkl', 'rb') as f:
    my_face_encoding = pickle.load(f)

timeout = 60  # Timeout before locking
face_not_detected_time = None
enabled = False  # Start disabled by default

# Placeholder for the pystray icon, updated later
icon = None

# Function to start face recognition
def start_face_recognition():
    global face_not_detected_time
    while enabled:
        cap = cv2.VideoCapture(0)  # Open the webcam
        ret, frame = cap.read()
        if not ret:
            cap.release()
            continue

        # Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
        rgb_frame = frame[:, :, ::-1]

        # Detect face locations and encode faces in the frame
        face_locations = face_recognition.face_locations(rgb_frame)
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

        face_recognized = False
        for face_encoding in face_encodings:
            # Compare the detected face with your saved encoding
            matches = face_recognition.compare_faces([my_face_encoding], face_encoding, tolerance=0.65)
            if matches[0]:
                face_recognized = True
                break

        if face_recognized:
            face_not_detected_time = None
            print("Your face is detected.")
        else:
            if face_not_detected_time is None:
                face_not_detected_time = time.time()
            elif time.time() - face_not_detected_time >= timeout:
                print("Your face is not detected. Locking screen in 15 seconds.")
                time.sleep(15)  # Warning delay
                lock_screen()
                break

        cap.release()  # Release the camera after the check

        # Wait for 5 minutes before the next check (300 seconds)
        time.sleep(300)

def toggle_face_recognition():
    global enabled
    enabled = not enabled
    if enabled:
        print("Face recognition enabled.")
        threading.Thread(target=start_face_recognition).start()
    else:
        print("Face recognition disabled.")
    update_icon()

def lock_screen():
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Update the menu bar icon when the state changes
def update_icon():
    if enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "Face Off: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "Face Off: Disabled"

# Function to quit the app
def quit_app(icon, item):
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Face Recognition', toggle_face_recognition),
        item('Quit', quit_app)
    )
    icon = Icon("Face Off", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle face recognition
keyboard.add_hotkey('cmd+shift+x', toggle_face_recognition)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the script running indefinitely
keyboard.wait()

Conclusion:

In this updated script:

• The face recognition will check every 5 minutes with time.sleep(300).
• The line time.sleep(0.1) has been removed, as it's unnecessary in this new interval-based setup.

If the script doesn't detect your face and prints "No face detected, try again," the issue may be related to a few common causes. Here's how you can troubleshoot and improve the face detection:

Possible Issues and Solutions:

1. Lighting Conditions:

   • Ensure you are in a well-lit environment. Poor lighting can cause the face detection algorithm to fail, especially if shadows obscure your face.

2. Camera Resolution:

   • By default, the script uses whatever resolution your webcam provides, which might be too low for accurate detection. You can try increasing the resolution of the capture.

3. Face Too Far from the Camera:

   • The algorithm might fail if your face is too far from the camera. Try moving closer and ensuring your face fills a larger portion of the frame.

4. Camera Not Capturing Properly:

   • The camera might not be initialized or capturing frames correctly. Confirm that cv2.VideoCapture(0) is working by displaying the captured frame.

Suggested Improvements

1. Increase Resolution: Set the resolution of the webcam to increase the chances of face detection.

2. Display the Captured Frame: You can display the webcam feed to ensure it's working correctly and that your face is visible.

Here’s an improved version of your script:

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import numpy as np
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    
    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)
    
    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Detect face locations and encode your face
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

if face_encodings:
    # Save the face encoding to a file
    with open('your_face_encoding.pkl', 'wb') as f:
        pickle.dump(face_encodings[0], f)
    print("Face encoding saved successfully!")
else:
    print("No face detected, try again.")

cap.release()
cv2.destroyAllWindows()

Key Changes:

1. Higher Resolution: I set the resolution to 1280x720 for better face detection.
2. Show Camera Feed: You can see the live camera feed in a window. Once you are happy with the view (with your face clearly visible), press 'q' to take a snapshot for face encoding.
3. Error Handling: The script will now exit gracefully if the webcam fails to capture frames.

Steps:

1. Run the script and ensure your face is visible in the window.
2. Press 'q' to take a snapshot and generate your face encoding.

If it still fails, you can try moving closer to the camera or adjusting the lighting conditions. Let me know if it works!

The issue you're describing indicates that the window displaying the webcam feed is not behaving as expected in macOS. By default, OpenCV does not handle window management very well on macOS, and the cv2.imshow() function can cause these types of problems where the close button is not functioning properly.

To resolve this and allow the script to continue, you can try the following:

1. Use a specific key press to close the window and continue: This will allow you to press a key (e.g., 'q') to capture the image and exit the window.

2. Add an additional wait period for proper event handling: OpenCV’s cv2.waitKey() function needs to be properly handled in a loop so it can detect your keyboard input.

Here’s an updated version of the script to address both issues:

Updated Script:

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import numpy as np
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    
    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)
    
    # Use 'q' to capture and proceed with face detection
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Detect face locations and encode your face
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

if face_encodings:
    # Save the face encoding to a file
    with open('your_face_encoding.pkl', 'wb') as f:
        pickle.dump(face_encodings[0], f)
    print("Face encoding saved successfully!")
else:
    print("No face detected, try again.")

cap.release()
cv2.destroyAllWindows()

Key Changes:

1. Exit on 'q': The script will now wait for you to press 'q' to capture the image and close the window properly.
2. Proper Cleanup: cv2.destroyAllWindows() is used to ensure that all OpenCV windows are properly closed after capturing the image.

How to Use:

1. Run the script.
2. Ensure that your face is visible in the window.
3. Press 'q' to take a snapshot.
4. The script will then close the window, proceed with encoding your face, and save the encoding to your_face_encoding.pkl.

This should resolve the issue with the unresponsive window and allow you to proceed smoothly. Let me know if this works for you!

The error you encountered stems from how the face_recognition library calls compute_face_descriptor() and the types of arguments being passed to it. Specifically, it seems there is a mismatch in how the face landmarks are being provided to the function.

How to Fix It:

In your script, you're passing the face_locations variable (which contains bounding boxes) directly to face_recognition.face_encodings(). However, this function expects the landmarks of the face, not just the bounding boxes.

To fix this, you should ensure that you're providing the correct face landmarks to the face_encodings() function.

Updated Code to Handle the Issue:

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import numpy as np
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    
    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)
    
    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Detect face locations and face landmarks
face_locations = face_recognition.face_locations(rgb_frame)
face_landmarks_list = face_recognition.face_landmarks(rgb_frame)

# Check if we have a face detected
if face_locations:
    # Now encode the face using face_encodings()
    face_encodings = face_recognition.face_encodings(rgb_frame, known_face_locations=face_locations)
    
    if face_encodings:
        # Save the face encoding to a file
        with open('your_face_encoding.pkl', 'wb') as f:
            pickle.dump(face_encodings[0], f)
        print("Face encoding saved successfully!")
    else:
        print("Error: Face detected, but unable to generate face encoding.")
else:
    print("No face detected, try again.")

cap.release()
cv2.destroyAllWindows()

Explanation of Changes:

1. face_landmarks_list: I added face_landmarks_list just to ensure that face landmarks are calculated. Though not directly needed for face encoding, it confirms the correct processing of faces.
2. known_face_locations Parameter: The face_encodings() function now explicitly receives known_face_locations=face_locations, ensuring the face location is correctly passed.
3. Error Handling: Improved error handling to check if face encodings are actually generated.

Instructions:

1. Run the script.
2. Make sure your face is visible in the camera preview.
3. Press 'q' to capture your face and generate the encoding.
4. If successful, the face encoding will be saved as your_face_encoding.pkl.

If it still doesn’t detect your face or fails to generate the .pkl file, check the lighting, distance from the camera, and ensure the camera is properly capturing the image.

The error you're encountering is due to the function face_recognition.face_encodings() receiving incompatible arguments related to the face detection and encoding. Specifically, the error shows that the function expects full facial landmarks, but you're providing face locations (bounding boxes). The face_encodings function relies on facial landmarks, which are required for the face descriptor.

Let’s correct this by ensuring that the correct landmarks are passed to the encoding function.

Here is the corrected approach:

1. Use face_locations to detect faces.
2. Use face_recognition.face_landmarks() to get the facial landmarks.
3. Pass these landmarks to face_recognition.face_encodings().

Updated Code:

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import numpy as np
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    
    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)
    
    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Detect face locations
face_locations = face_recognition.face_locations(rgb_frame)

# Check if any face was detected
if face_locations:
    # Detect landmarks for the first face
    face_landmarks = face_recognition.face_landmarks(rgb_frame, face_locations)

    # Now encode the face using face_encodings()
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    if face_encodings:
        # Save the face encoding to a file
        with open('your_face_encoding.pkl', 'wb') as f:
            pickle.dump(face_encodings[0], f)
        print("Face encoding saved successfully!")
    else:
        print("Error: Face detected, but unable to generate face encoding.")
else:
    print("No face detected, try again.")

cap.release()
cv2.destroyAllWindows()

Key Changes:

1. Face Locations Detection: face_recognition.face_locations(rgb_frame) is used to detect the bounding boxes of faces.
2. Landmarks and Encoding: face_landmarks and face_encodings() work together to generate the correct encoding for the detected face.
3. Error Handling: Added checks to ensure a face is detected before attempting to generate an encoding.

Instructions:

1. Run the script.
2. Look at the camera and ensure your face is visible.
3. Press 'q' to capture the frame.
4. If successful, the face encoding will be saved as your_face_encoding.pkl.

This approach should prevent the error you're experiencing and properly generate the encoding file. Let me know if it works!

The error message you're encountering indicates a mismatch between the arguments you're passing to the face_recognition.face_encodings() function and what the function is expecting.

It seems like you're passing the bounding boxes (face locations) directly to the function, but the function requires full face landmarks or proper face detections as full_object_detections.

Here's how to proceed:

1. Ensure Proper Detection of Faces: Instead of just getting face locations, we need to ensure that the detected faces and their landmarks are properly passed to the face encoding function.

2. Avoid Using Raw Bounding Boxes: The encoding function is expecting either a list of full object detections (which includes the landmarks) or nothing, in which case it will automatically detect faces.

Let's rewrite the script to handle this properly by allowing face_recognition.face_encodings() to automatically handle face detection and encoding.

Updated Script (Simplified):

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)

    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Automatically detect faces and generate encodings
face_encodings = face_recognition.face_encodings(rgb_frame)

# Check if any face was detected and encoded
if face_encodings:
    # Save the face encoding to a file
    with open('your_face_encoding.pkl', 'wb') as f:
        pickle.dump(face_encodings[0], f)
    print("Face encoding saved successfully!")
else:
    print("No face detected, try again.")

cap.release()

Key Changes:

1. No Need for Face Locations: The function face_recognition.face_encodings() can automatically detect the faces in the frame and generate the encodings without manually providing face locations.

2. Simplified Code: We have removed the extra complexity of trying to detect face landmarks manually. The face_encodings() function does everything in one step, detecting the faces and encoding them.

Steps:

1. Run the script.
2. Make sure your face is visible in the camera.
3. Press 'q' to capture the image.
4. If successful, it will save the face encoding to your_face_encoding.pkl.

Additional Notes:

• Ensure that your webcam is working and that you're in a well-lit environment.
• If the face is not detected, try adjusting the lighting or getting closer to the camera.

This approach simplifies the process and should resolve the error you're encountering. Let me know how it works for you!

The issue you're encountering arises from the incorrect use of the face_recognition.face_encodings() function. Specifically, face_recognition.face_encodings() expects face_locations but does not require facial landmarks as a separate argument.

The problem might also be caused by how OpenCV and face_recognition work together with dlib. Let's try the simplest approach: allowing face_recognition to handle both face detection and encoding automatically without passing face_landmarks.

Here's the corrected version of your code:

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import numpy as np
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    
    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)
    
    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Automatically detect face locations and generate encodings
face_encodings = face_recognition.face_encodings(rgb_frame)

# Check if any face was detected and encoded
if face_encodings:
    print("Face encoding generated successfully!")
    # Save the face encoding to a file
    with open('your_face_encoding.pkl', 'wb') as f:
        pickle.dump(face_encodings[0], f)
    print("Face encoding saved successfully!")
else:
    print("No face detected, try again.")

cap.release()

Key Points:

1. Automatic Face Detection and Encoding: We removed the separate call to face_landmarks and simply allowed face_recognition.face_encodings() to automatically handle both detection and encoding in one step.

2. Error Handling: The script checks if any face encodings are generated, and if not, it informs the user to try again.

Instructions:

1. Run the script.
2. Look at the camera, ensuring your face is well-lit.
3. Press 'q' to capture the frame.
4. If successful, the face encoding will be saved as your_face_encoding.pkl.

This should resolve the problem where the face encoding is not being generated or saved correctly. Let me know if this works for you!

Yes, passing rgb_frame (the frame converted from BGR to RGB) is indeed the correct approach when using the face_recognition.face_encodings() method. The face_recognition library expects the image data to be in RGB format, and cv2.VideoCapture() in OpenCV captures images in BGR format by default. Therefore, converting the frame to RGB before passing it to face_recognition.face_encodings() is the correct step.

Here’s why:

1. OpenCV and BGR Format: By default, OpenCV reads images and frames from video in BGR format (blue, green, red).
2. face_recognition and RGB Format: The face_recognition library expects images in RGB format (red, green, blue).

To pass the correct image format to face_recognition.face_encodings(), we need to convert the OpenCV frame from BGR to RGB using the following code:

python
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rgb_frame = frame[:, :, ::-1]

This line flips the color channels to convert BGR to RGB.

Confirming the face_image Argument:

The face_image argument in face_recognition.face_encodings() expects an image as a NumPy array in the RGB format, which is exactly what rgb_frame represents after the conversion.

python
Copy code
face_encodings = face_recognition.face_encodings(rgb_frame)

This approach is correct, and passing rgb_frame ensures that the face encoding process receives the image in the required format.

Conclusion:

• Yes, passing rgb_frame is the correct thing to do.
• The conversion from BGR to RGB is necessary because cv2.VideoCapture() gives you a BGR image, but face_recognition expects an RGB image.

If you're still encountering issues, it may be due to lighting conditions or how the face is positioned in the frame rather than the format of the image.

The error you are encountering stems from a mismatch between the expected input types for the face_recognition.face_encodings() function and what is actually being passed to it. This issue occurs when the face_recognition.face_encodings() method is internally invoking dlib to calculate face descriptors, and it's expecting full face landmark detections but receiving incorrect input.

Potential Causes:

1. Known Face Locations: When providing the known_face_locations argument, it should be bounding boxes (locations) of the faces in the image, not full landmarks or full_object_detection.
2. Face Landmarks: The face_encodings function works directly with the face bounding boxes (face_locations) or automatically detects faces if no bounding boxes are provided.

Fix: Ensure Correct Input for face_recognition.face_encodings

Here’s how to resolve the issue by simplifying the input to face_encodings():

Corrected Code:

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)

    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Detect face locations (bounding boxes) in the image
face_locations = face_recognition.face_locations(rgb_frame)

# Ensure a face is detected
if face_locations:
    print("Face detected!")

    # Generate face encodings using the face locations
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    if face_encodings:
        print("Face encoding generated successfully!")

        # Save the face encoding to a file
        with open('your_face_encoding.pkl', 'wb') as f:
            pickle.dump(face_encodings[0], f)
        print("Face encoding saved successfully!")
    else:
        print("Error: Face detected, but unable to generate face encoding.")
else:
    print("No face detected, try again.")

cap.release()

Key Points:

1. Use face_recognition.face_locations(): This function detects face locations (bounding boxes) in the image. These locations are passed to face_recognition.face_encodings() as the second argument.
2. face_encodings: This method requires the bounding box (face location) as the second argument. If no face location is provided, it will attempt to detect faces on its own.
3. Face Detected Check: The code first checks if faces are detected before attempting to generate encodings.

Why This Works:

• The face_locations variable is a list of tuples containing the bounding box coordinates for each detected face in the image. This is exactly what the face_encodings() method expects.
• The face_encodings() method can now process the face locations properly without invoking the incompatible function.

Steps:

1. Run the script.
2. Ensure your face is visible in the webcam.
3. Press 'q' to capture the image.
4. If a face is detected, the face encoding will be saved to your_face_encoding.pkl.

This should resolve the TypeError you're encountering with compute_face_descriptor(). Let me know if it works!

You're correct that my explanation may not have clearly resolved the underlying issue you're facing. Let's focus on the actual cause of the problem:

The Error:

The error you're encountering suggests that the underlying dlib library is receiving incompatible input, specifically related to the landmarks or bounding boxes (face_locations) passed to face_recognition.face_encodings().

This is because the face_recognition.face_encodings() function internally calls dlib's compute_face_descriptor() and expects the face_locations to be in a specific format.

Possible Causes:

1. Invalid or empty face locations: Even though the code captures the frame and passes face_locations to the face_encodings() function, the locations detected by face_locations = face_recognition.face_locations(rgb_frame) might be incorrect, empty, or corrupted.
2. Incorrect version of dlib or face_recognition: There could be a version mismatch between the dlib and face_recognition libraries, causing the input types to be incorrectly interpreted.

Approach to Resolve the Issue:

1. Check for Valid Face Locations: Ensure that face_locations is actually being populated with valid data. Print the face_locations variable to confirm that bounding boxes are detected.

2. Ensure Proper Library Installation: Make sure that both dlib and face_recognition are properly installed and compatible.

Code with Detailed Debugging:

To debug the issue and ensure the correct flow, let's add print statements and checks to see what exactly is going wrong:

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)

    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Detect face locations (bounding boxes) in the image
face_locations = face_recognition.face_locations(rgb_frame)

# Print face locations to debug
print(f"Detected face locations: {face_locations}")

# Ensure a face is detected
if face_locations:
    print("Face detected!")

    try:
        # Generate face encodings using the face locations
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

        if face_encodings:
            print("Face encoding generated successfully!")

            # Save the face encoding to a file
            with open('your_face_encoding.pkl', 'wb') as f:
                pickle.dump(face_encodings[0], f)
            print("Face encoding saved successfully!")
        else:
            print("Error: Face detected, but unable to generate face encoding.")

    except Exception as e:
        print(f"Error during face encoding: {e}")
else:
    print("No face detected, try again.")

cap.release()

Debugging Steps:

1. Check if face locations are detected: This line prints the face_locations:

   python
   Copy code
   print(f"Detected face locations: {face_locations}")
   

   This will help you see if the face detection is working properly.

2. Check for the encoding step: By catching exceptions during the encoding process:

   python
   Copy code
   except Exception as e:
       print(f"Error during face encoding: {e}")
   

   This will help debug whether it's an issue with how face_locations is processed by face_encodings().

Next Steps:

1. Print the Output: Run the script and observe whether valid face_locations are being detected.
2. Check the Versions: Make sure your dlib and face_recognition libraries are up to date:

   bash
   Copy code
   pip install --upgrade dlib face_recognition
   

If valid face locations are detected but the encoding still fails, we can troubleshoot further based on that output. Let me know the result of this debugging!

The error you're encountering happens because face_recognition.face_encodings() internally calls dlib's compute_face_descriptor() function, which expects specific types of input. The error suggests that there's a mismatch between the face location (bounding box) and the expected face landmarks that are passed to compute_face_descriptor().

This mismatch likely stems from how the face_locations are being passed into face_encodings().

Solution:

We need to ensure that the face_locations argument is correctly processed by dlib. Sometimes, manually specifying the model='large' or model='small' for more accurate landmark detection can help. Additionally, reducing the number of jitters (num_jitters=0 by default) can simplify the process.

Try the following modifications to the code:

1. Use the 'large' model for more accurate facial landmark detection.
2. Check for multiple faces (even though we expect one).
3. Set num_jitters=0 to simplify the computation.

Here’s the modified code:

Updated Code:

python
Copy code
#!/usr/bin/env python3

import face_recognition
import cv2
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)

    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Detect face locations (bounding boxes) in the image
face_locations = face_recognition.face_locations(rgb_frame, model='large')

# Print face locations to debug
print(f"Detected face locations: {face_locations}")

# Ensure a face is detected
if face_locations:
    print("Face detected!")

    try:
        # Generate face encodings using the face locations with `num_jitters=0` and `model='large'`
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations, num_jitters=0, model='large')

        if face_encodings:
            print("Face encoding generated successfully!")

            # Save the face encoding to a file
            with open('your_face_encoding.pkl', 'wb') as f:
                pickle.dump(face_encodings[0], f)
            print("Face encoding saved successfully!")
        else:
            print("Error: Face detected, but unable to generate face encoding.")

    except Exception as e:
        print(f"Error during face encoding: {e}")
else:
    print("No face detected, try again.")

cap.release()

Explanation of Changes:

1. Model: I specified the model='large' for detecting more accurate facial landmarks. You can also try the 'small' model for a quicker but less accurate detection.
2. Number of Jitters: I set num_jitters=0 to simplify the encoding process.
3. Error Handling: If the process fails during encoding, the exception is caught and the error is printed.

Steps:

1. Run the script.
2. Look at the camera and press 'q' to capture the frame.
3. If a face is detected, it will generate and save the face encoding to your_face_encoding.pkl.

Let me know how this works and if the error persists!

Yes, you're on the right track. The error message suggests that there is a type mismatch in the arguments being passed to compute_face_descriptor(). Specifically, face_recognition.face_encodings() is internally invoking compute_face_descriptor(), but the image or landmark data being passed is not in the format dlib expects.

The line array([[[219, 187, 164],... is a NumPy array representing the image in RGB format, which is expected. However, dlib is expecting full face landmarks (dlib's full_object_detection) when calculating face descriptors, not just the bounding box (face locations).

Breakdown of the Issue:

• What the function expects:
  • face_recognition.face_encodings() expects the image and optionally the face locations (bounding boxes). It internally generates face landmarks (via dlib) and then passes those landmarks to the face descriptor computation.
• What you are passing:
  • You're passing rgb_frame (which is correct) and face_locations (which are bounding boxes). However, the internal dlib method compute_face_descriptor() expects full facial landmarks, not just bounding boxes.

What to Do:

We need to ensure that the landmarks are correctly detected and passed. Fortunately, the face_recognition library handles this internally when given only the image (without manually providing landmarks). Thus, the best course is to let face_recognition.face_encodings() handle everything automatically without manually specifying face_locations.

Updated Code (no manual face_locations):

python
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#!/usr/bin/env python3

import face_recognition
import cv2
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("Look at the camera to capture your face...")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow('Frame', frame)

    # Press 'q' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
rgb_frame = frame[:, :, ::-1]

# Directly calculate face encodings without manually passing face locations
try:
    # Let face_recognition automatically detect faces and generate encodings
    face_encodings = face_recognition.face_encodings(rgb_frame)

    if face_encodings:
        print("Face encoding generated successfully!")

        # Save the face encoding to a file
        with open('your_face_encoding.pkl', 'wb') as f:
            pickle.dump(face_encodings[0], f)
        print("Face encoding saved successfully!")
    else:
        print("No face detected or unable to generate face encoding.")

except Exception as e:
    print(f"Error during face encoding: {e}")

cap.release()

Explanation:

1. Let face_recognition handle face detection: We are no longer manually passing face_locations because it seems that’s where the issue is. face_recognition.face_encodings() can handle both detection and encoding in one step.

2. Simplified Flow: By removing the manual step of detecting face locations, this ensures that the right types (e.g., dlib.full_object_detection landmarks) are passed internally to compute_face_descriptor().

Why This Works:

• The face_recognition.face_encodings() method internally handles the detection of face landmarks and passes them in the correct format for encoding, ensuring no type mismatch.

Next Steps:

1. Run the script and capture your face by pressing 'q'.
2. The face encoding should now be generated and saved in your_face_encoding.pkl.

Let me know if this resolves the issue!

The error you're encountering is still related to a mismatch between what dlib expects as input for compute_face_descriptor() and what is being passed from the face_recognition library. Specifically, the error is indicating that a NumPy array (the image) is being passed where dlib expects a full_object_detection object (landmarks).

This indicates that the face_recognition.face_encodings() function isn't properly creating the face landmarks necessary for the compute_face_descriptor() function to work.

Possible Reasons:

1. Incorrect Installation or Version Mismatch: If dlib and face_recognition are not properly installed or if there's a version mismatch, this can cause this type of issue.
2. Underlying dlib Installation Problem: The root cause may be an issue with how dlib was built or installed.

Steps to Resolve:

1. Check the Version Compatibility:

Ensure that both dlib and face_recognition are properly installed and compatible. You can check the versions with the following commands:

bash
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pip show dlib
pip show face_recognition

Make sure they are up-to-date:

bash
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pip install --upgrade dlib face_recognition

2. Reinstall dlib and face_recognition:

It might be helpful to reinstall both libraries to resolve any installation issues. Use the following commands:

bash
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pip uninstall dlib face_recognition
pip install dlib face_recognition

3. Use the CPU Version of dlib (If applicable):

If you installed a GPU-accelerated version of dlib, try switching to the CPU version, as there may be some conflicts in the GPU-accelerated version:

bash
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pip install dlib

4. Run a Minimal Working Example:

To isolate the issue, try running a very basic face recognition example using a static image. This will help determine whether the problem is related to webcam input or the libraries themselves.

python
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import face_recognition

# Load a sample picture and learn how to recognize it.
image = face_recognition.load_image_file("sample.jpg")
face_encodings = face_recognition.face_encodings(image)

if face_encodings:
    print("Face encoding generated successfully!")
else:
    print("No face encoding generated.")

If this simple script works, the issue may be tied to the webcam input processing. If it also fails, the problem lies with the dlib installation.

5. Verify dlib Build:

If none of the above works, you might need to rebuild dlib from source, particularly if you're using a custom environment like conda:

bash
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pip uninstall dlib
git clone https://github.com/davisking/dlib.git
cd dlib
python setup.py install

This rebuilds dlib and resolves any build-related issues.

Please try these steps and let me know if the issue persists after reinstalling and running a basic test.

It appears that both dlib and face_recognition are installed correctly and are up-to-date. Given that the versions seem compatible, the issue may be related to how the face landmarks are being detected and passed to dlib's compute_face_descriptor function.

Here’s a simplified approach to ensure everything is functioning as expected:

Step 1: Simplify the Problem by Testing with a Static Image

To isolate whether the issue lies with the webcam input or with the encoding process, try running a basic face encoding on a static image. This can help confirm if the problem is specifically related to the webcam capture.

Save a picture of yourself (e.g., your_photo.jpg) and try the following code:

python
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import face_recognition
import pickle

# Load the image
image = face_recognition.load_image_file("your_photo.jpg")

# Detect face locations
face_locations = face_recognition.face_locations(image)

# Ensure that at least one face is detected
if face_locations:
    print(f"Face detected at locations: {face_locations}")

    # Generate face encodings
    face_encodings = face_recognition.face_encodings(image, face_locations)

    # Check if face encodings were generated
    if face_encodings:
        print("Face encoding generated successfully!")
        
        # Save the face encoding to a file
        with open('your_face_encoding.pkl', 'wb') as f:
            pickle.dump(face_encodings[0], f)
        print("Face encoding saved successfully!")
    else:
        print("Error: Face detected, but unable to generate face encoding.")
else:
    print("No face detected.")

Step 2: If This Works, Investigate the Webcam Input

If the above code runs without error and successfully generates a face encoding, the problem is likely with how the webcam input is being processed or the quality of the frames captured from the webcam. The webcam might be producing frames that dlib has trouble processing due to low resolution or incorrect frame formatting.

Step 3: Use Lower Resolution and Verify Webcam Frame Quality

Try lowering the resolution of the webcam to reduce potential frame format issues:

python
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

Step 4: Print and Verify Webcam Frame Quality

To ensure that the webcam is capturing frames of suitable quality, print the shape of the rgb_frame just before processing:

python
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print(f"Frame shape: {rgb_frame.shape}")

Let me know if the static image test works successfully and if the issue is specifically tied to the webcam input!

The error message indicates that CMake is not installed or is incorrectly installed on your system. CMake is required to build dlib from source, and its absence is causing the build to fail.

Here’s how to resolve the issue:

Step 1: Install CMake

1. For macOS (if using Homebrew):

   bash
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   brew install cmake
   

2. For Ubuntu/Debian-based systems:

   bash
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   sudo apt update
   sudo apt install cmake
   

3. For Redhat/CentOS-based systems:

   bash
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   sudo yum install cmake
   

4. For Windows:

   • Download CMake from the official website: https://cmake.org/download/
   • Install it and ensure that you add CMake to your system's PATH during installation.

Step 2: Verify Installation

Once you’ve installed CMake, check that it’s properly installed by running:

bash
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cmake --version

This should output the version of CMake installed on your system.

Step 3: Reinstall dlib

After confirming that CMake is properly installed, try reinstalling dlib:

bash
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pip uninstall dlib
pip install dlib

This should allow dlib to be built successfully.

Step 4: Build dlib from Source (if needed)

If pip install dlib still fails after installing CMake, you can manually build dlib from source by following these steps:

1. Clone the dlib repository:

   bash
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   git clone https://github.com/davisking/dlib.git
   cd dlib
   

2. Install dlib with CMake:

   bash
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   python setup.py install
   

This should resolve the CMake-related issue and successfully install dlib. Let me know if this resolves the issue, or if there’s anything else you'd like help with!

To reinstall Python using Homebrew on macOS, you can follow these steps:

Step 1: Uninstall the Current Python Installation

First, uninstall the current version of Python installed via Homebrew:

bash
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brew uninstall python

Step 2: Reinstall Python

Once Python is uninstalled, reinstall it using Homebrew:

bash
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brew install python

Step 3: Verify Python Installation

After reinstalling, verify the installation by checking the Python version:

bash
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python3 --version

You should see the newly installed Python version. Homebrew typically installs the latest version of Python 3.

Step 4: Update the PATH (If Needed)

Homebrew should automatically update the PATH to use the new version of Python, but if you encounter issues, you can manually add the Homebrew-installed Python to your PATH by adding this line to your .bash_profile or .zshrc file:

bash
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export PATH="/usr/local/opt/python/libexec/bin:$PATH"

After adding the line, reload the profile with:

bash
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source ~/.bash_profile  # For bash
source ~/.zshrc         # For zsh

This should complete the process of reinstalling Python via Homebrew. Let me know if you encounter any issues!

To update Homebrew and its installed packages, follow these steps:

Step 1: Update Homebrew

First, update Homebrew itself:

bash
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brew update

Step 2: Upgrade Installed Packages

After updating Homebrew, you can upgrade all of the installed packages (including Python if it’s outdated):

bash
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brew upgrade

Step 3: Clean Up Old Versions

You can also remove outdated versions of installed packages that are no longer needed:

bash
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brew cleanup

This ensures your system is using the latest versions of packages managed by Homebrew. Let me know if you encounter any issues during the update process!

To debug and identify the cause of the segmentation fault in your script, you can introduce logging at key points and carefully manage resources like the webcam (which could be causing the issue). Here’s how you can proceed:

1. Add Debugging Messages

I will add print() statements throughout the code to help trace where the issue occurs. These will help you locate where the segfault happens.

2. Open/Close the Webcam Outside the Loop

Repeatedly opening and closing the webcam inside the loop might lead to resource issues. Instead, open it once, use it throughout the loop, and close it after you're done.

Updated Code with Debugging:

python
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#!/usr/bin/env python3

import cv2
import time
import os
import keyboard
import threading
import face_recognition
import pickle
from pystray import Icon, MenuItem as item, Menu
from PIL import Image

# Load your face encoding
print("Loading face encoding...")
with open('your_face_encoding.pkl', 'rb') as f:
    my_face_encoding = pickle.load(f)

timeout = 60  # Timeout before locking
face_not_detected_time = None
enabled = False  # Start disabled by default

# Placeholder for the pystray icon, updated later
icon = None

# Function to start face recognition
def start_face_recognition():
    global face_not_detected_time
    try:
        print("Starting face recognition...")
        cap = cv2.VideoCapture(0)  # Open the webcam
        if not cap.isOpened():
            print("Error: Could not open the webcam.")
            return
        
        while enabled:
            print("Reading from webcam...")
            ret, frame = cap.read()
            if not ret:
                print("Failed to capture frame. Retrying...")
                time.sleep(1)  # Sleep for a moment before retrying
                continue

            # Convert frame from BGR (OpenCV format) to RGB (face_recognition format)
            rgb_frame = frame[:, :, ::-1]

            # Detect face locations and encode faces in the frame
            print("Detecting face locations...")
            face_locations = face_recognition.face_locations(rgb_frame)
            print(f"Detected face locations: {face_locations}")

            face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
            print(f"Detected face encodings: {len(face_encodings)} faces found.")

            face_recognized = False
            for face_encoding in face_encodings:
                # Compare the detected face with your saved encoding
                print("Comparing detected face to saved encoding...")
                matches = face_recognition.compare_faces([my_face_encoding], face_encoding, tolerance=0.65)
                if matches[0]:
                    print("Face recognized.")
                    face_recognized = True
                    break

            if face_recognized:
                face_not_detected_time = None
                print("Your face is detected.")
            else:
                if face_not_detected_time is None:
                    face_not_detected_time = time.time()
                elif time.time() - face_not_detected_time >= timeout:
                    print("Your face is not detected. Locking screen in 15 seconds.")
                    time.sleep(15)  # Warning delay
                    lock_screen()
                    break

            # Wait for 5 minutes before the next check (300 seconds)
            print("Waiting for 5 minutes before next check...")
            time.sleep(300)
        
    except Exception as e:
        print(f"Error in face recognition: {e}")
    finally:
        print("Releasing webcam...")
        cap.release()  # Release the camera after we're done

def toggle_face_recognition():
    global enabled
    enabled = not enabled
    if enabled:
        print("Face recognition enabled.")
        threading.Thread(target=start_face_recognition).start()
    else:
        print("Face recognition disabled.")
    update_icon()

def lock_screen():
    print("Locking the screen...")
    os.system("osascript -e 'tell application \"System Events\" to keystroke \"q\" using {control down, command down}'")

# Update the menu bar icon when the state changes
def update_icon():
    if enabled:
        icon.icon = Image.open("enabled_icon.png")
        icon.title = "Face Off: Enabled"
    else:
        icon.icon = Image.open("disabled_icon.png")
        icon.title = "Face Off: Disabled"

# Function to quit the app
def quit_app(icon, item):
    print("Quitting the application...")
    icon.stop()

# Set up pystray for the menu bar icon
def setup_icon():
    global icon
    menu = Menu(
        item('Toggle Face Recognition', toggle_face_recognition),
        item('Quit', quit_app)
    )
    icon = Icon("Face Off", Image.open("disabled_icon.png"), menu=menu)
    icon.run()

# Hotkey to toggle face recognition
keyboard.add_hotkey('cmd+shift+x', toggle_face_recognition)

# Run the menu bar icon in a separate thread
threading.Thread(target=setup_icon).start()

# Keep the script running indefinitely
keyboard.wait()

Key Debugging and Resource Management Improvements:

1. Open/Close Webcam Once: The webcam (cv2.VideoCapture(0)) is now opened once before the loop and closed at the end, avoiding multiple open/close calls that might cause resource issues.
2. Debugging Messages: Added print() statements throughout the code to track the program’s execution and pinpoint where the issue arises.
3. Error Handling: Wrapped the webcam capture and face recognition loop in a try-except-finally block to handle errors gracefully and ensure the webcam is released in the finally block.
4. Thread Debugging: Added debugging for when the thread starts and the recognition is enabled or disabled.

Steps to Debug:

1. Run the script: Look for the printed messages in the terminal to see where it crashes.
2. Segfault Location: If a segmentation fault happens, the last printed message will indicate where the issue occurred, helping us diagnose the exact function or operation that caused it.

Common Causes of Segfaults:

• Webcam issues: If the webcam is repeatedly opened and closed, it can lead to resource exhaustion or driver issues. This script avoids that by managing the webcam properly.
• Memory issues: If frames are large and you’re using a lot of them in memory, a segmentation fault could occur. Make sure your system has enough resources.

Let me know the last message that prints before the segfault, and I can help further!

The segmentation fault occurring when importing keyboard in ipython suggests there may be an issue with how the keyboard package interacts with the environment or terminal.

Here are a few steps to help resolve this issue:

1. Check for System-Specific Issues

The keyboard package has some limitations on macOS and Linux because it requires special permissions to monitor global keyboard events. The segmentation fault might be related to this. Try running the script outside of ipython directly in a normal Python environment or terminal:

bash
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python script.py

2. Ensure keyboard Package Permissions

The keyboard package requires elevated permissions to intercept system-level key events. On macOS, this often means you need to grant accessibility permissions to Python or your terminal.

1. Go to System Preferences > Security & Privacy.
2. Select the Privacy tab.
3. On the left, choose Accessibility.
4. Add your terminal or Python interpreter to the list by clicking the lock and the "+" button, then selecting your terminal or Python.

3. Try an Alternative for MacOS

If keyboard continues to cause issues, consider using an alternative library for hotkeys, such as pynput, which may be more stable on macOS:

bash
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pip install pynput